Abstract： Technological parameters in fused deposition modeling three-dimensional printing are coupled and the forming process is a non-linear forming process. A large number of modeling parameters affect the quality of the product precision in fused deposition modeling of three-dimensional printing. In order to clarify the effects of various parameters on the forming precision and improve the precision of three-dimensional printing, the wavelet neural network prediction model of product precision was build by using the Matlab software. The arithmetic was designed and the samples were acquired by fused deposition modeling experiment. The training samples were used to train the network to accomplish the mapping relation between the input and output of the network. The test samples were used to verify the performance of the trained network. Simulation results indicate that the prediction model has sufficient accuracy. The prediction model of product precision is feasible and valid in theory and in practice. The wavelet neural network method was used to model the relation between the processing parameters and the product precision in fused deposition modeling of threedimensional printing. The difficult problem to create the accurate mathematics model was solved.
纪良波. 基于小波神经网络熔丝堆积三维打印精度预测模型[J]. 上海交通大学学报（自然版）, .
JI Liangbo. Precision Prediction Model in Fused Deposition Modeling of Three-Dimensional Printing Based on Wavelet Neural Network. J. Shanghai Jiaotong Univ.(Sci.) , 2015, 49(03): 375-378.
［1］余东满, 李晓静, 王笛. 熔融沉积快速成型工艺过程分析及应用［J］. 机械设计与制造，2011(8)：65267.YU Dongman，LI Xiaojing，WANG Di. Process analysis and application for rapid prototyping based on fused deposition modeling ［J］. Machinery Design & Manufacture，2011(8)：65267.［2］董海涛. 熔融沉积快速成型的工艺分析［J］. 制造技术与机床，2013(10)：9698.DONG Haitao. Analysis on rapid prototyping of fused deposition modeling ［J］. Manufacturing Technology & Machine Tool，2013(10)：9698.
［3］刘雪峰, 马胜军, 刘锦平, 等. Cu12 %Al合金高温压缩变形过程本构关系的BP神经网络模型［J］. 材料工程, 2009(1)：1014.LIU Xuefeng, MA Shengjun, LIU Jinping, et al. BP neural networks models for constitutive relationship during high temperature deformation process of Cu12%Al alloy ［J］. Journal of Materials Engineering, 2009(1)：1014.［4］姜淑娟, 刘伟军, 南亮亮. 基于神经网络的激光熔覆高度预测［J］. 机械工程学报，2009，45(3)：269274.JIANG Shujuan, LIU Weijun, NAN Liangliang. Laser cladding height prediction based on neural network ［J］. Journal of Mechanical Engineering, 2009, 45(3)：269274.［5］姜银方, 王飞, 李新城, 等. 基于正交试验和神经网络的激光拼焊板回弹预测［J］. 塑性工程学报, 2009, 16(3)：4044.JIANG Yinfang, WANG Fei, LI Xincheng, et al. Study on the springback prediction in laser TWBs forming based on orthogonal experiment and neural network ［J］. Journal of Plasticity Engineering, 2009, 16(3)：4044.［6］郭奕崇, 刘天青, 郭键, 等. 基于人工神经网络的注塑发泡实验参数分析与预测［J］. 塑料，2013，42(2)：104106.GUO Yichong，LIU Tianqing，GUO Jian, et al. Parameter analyzing and forecasting for injection foaming experiment based on artificial neural network ［J］. Plastics, 2013，42(2)：104106.［7］纪良波, 李永志, 陈爱霞. 基于神经网络和遗传算法的塑料热压成形多目标优化［J］. 塑料，2012，41(3)：9093.JI Liangbo, LI Yongzhi, CHEN Aixia. Multiobjective optimization of plastic thermoforming based on neural network and genetic algorithm ［J］. Plastics，2012，41(3)： 9093.［8］LI Yao, YU Fan, WU Chen. Application of neural network to downhill shift strategy for automatic transmission ［J］. Journal of Shanghai Jiaotong University: Science, 2010，15(4)：498503.［9］郑荣. 三维铝型材挤压模多参数CAO系统研究［D］. 南昌：南昌大学机电工程学院，2005.［10］肖胜中. 小波神经网络理论与应用［M］. 沈阳：东北大学出版社，2006.
